Mathematical Foundations of Neuroscience
This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important...
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| Hlavní autoři: | , , |
|---|---|
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2010. |
| Edice: | Interdisciplinary Applied Mathematics
35 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Mathematical foundations of neuroscience, G. Bard Ermentrout, David H. Terman, 2010, New York, Springer, 1 volume (xv-422 pages), Interdisciplinary applied mathematics, 978-0-387-87707-5 • Mathematical foundations of neuroscience, G. Bard Ermentrout, David H. Terman, 2010, New York, Springer, 1 volume (xv-422 pages), Interdisciplinary applied mathematics, 978-0-387-87707-5 • Mathematical Foundations of Neuroscience, Texte imprimé, 9780387877600 • Mathematical foundations of neuroscience, G. Bard Ermentrout, David H. Terman, 2010, New York, Springer, 1 volume (xv-422 pages), Interdisciplinary applied mathematics, 978-0-387-87707-5 • Mathematical foundations of neuroscience, G. Bard Ermentrout, David H. Terman, 2010, New York, Springer, 1 volume (xv-422 pages), Interdisciplinary applied mathematics, 978-0-387-87707-5 • Mathematical Foundations of Neuroscience, Texte imprimé, 9780387877600 • Mathematical foundations of neuroscience, G. Bard Ermentrout, David H. Terman, 2010, New York, Springer, 1 volume (xv-422 pages), Interdisciplinary applied mathematics, 978-0-387-87707-5 |
Obsah:
- The Hodgkin Huxley Equations Dendrites Dynamics The Variety of Channels Bursting Oscillations Propagating Action Potentials Synaptic Channels Neural Oscillators: Weak Coupling Neuronal Networks: Fast/Slow Analysis Noise Firing Rate Models Spatially Distributed Networks.